Search Results for author:
Found 51 papers, 19 papers with code
Building Optimal Neural Architectures using Interpretable Knowledge
Neural Architecture Search is a costly practice.
SIFiD: Reassess Summary Factual Inconsistency Detection with LLM
Ensuring factual consistency between the summary and the original document is paramount in summarization tasks.
Boosting of Thoughts: Trial-and-Error Problem Solving with Large Language Models
The reasoning performance of Large Language Models (LLMs) on a wide range of problems critically relies on chain-of-thought prompting, which involves providing a few chain of thought demonstrations as exemplars in prompts.
GOAt: Explaining Graph Neural Networks via Graph Output Attribution
Understanding the decision-making process of Graph Neural Networks (GNNs) is crucial to their interpretability.
CascadedGaze: Efficiency in Global Context Extraction for Image Restoration
Image restoration tasks traditionally rely on convolutional neural networks.
Ranked #1 on
Image Denoising
on SIDD
Instruction Fusion: Advancing Prompt Evolution through Hybridization
The fine-tuning of Large Language Models (LLMs) specialized in code generation has seen notable advancements through the use of open-domain coding queries.
AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness
Online advertisements are important elements in e-commerce sites, social media platforms, and search engines.
iHAS: Instance-wise Hierarchical Architecture Search for Deep Learning Recommendation Models
The searching stage identifies optimal instance-wise embedding dimensions across different field features via carefully designed Bernoulli gates with stochastic selection and regularizers.
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis leverages multimodal signals to detect the sentiment of a speaker.
TCR: Short Video Title Generation and Cover Selection with Attention Refinement
In this paper, we first collect and present a real-world dataset named Short Video Title Generation (SVTG) that contains videos with appealing titles and covers.
Search-Map-Search: A Frame Selection Paradigm for Action Recognition
To overcome the limitations of existing methods, we propose a Search-Map-Search learning paradigm which combines the advantages of heuristic search and supervised learning to select the best combination of frames from a video as one entity.
LA3: Efficient Label-Aware AutoAugment
Automated augmentation is an emerging and effective technique to search for data augmentation policies to improve generalizability of deep neural network training.
CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering
To address this issue, we propose CEIL, a novel Classification-Enhanced Iterative Learning framework for short text clustering, which aims at generally promoting the clustering performance by introducing a classification objective to iteratively improve feature representations.
GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation
Recent breakthroughs in the field of language-guided image generation have yielded impressive achievements, enabling the creation of high-quality and diverse images based on user instructions. Although the synthesis performance is fascinating, one significant limitation of current image generation models is their insufficient ability to generate text coherently within images, particularly for complex glyph structures like Chinese characters.
Optical Character Recognition (OCR)
Text-to-Image Generation
Reparameterization through Spatial Gradient Scaling
Reparameterization aims to improve the generalization of deep neural networks by transforming convolutional layers into equivalent multi-branched structures during training.
A General-Purpose Transferable Predictor for Neural Architecture Search
In this paper, we propose a general-purpose neural predictor for NAS that can transfer across search spaces, by representing any given candidate Convolutional Neural Network (CNN) with a Computation Graph (CG) that consists of primitive operators.
GENNAPE: Towards Generalized Neural Architecture Performance Estimators
In this paper, we propose GENNAPE, a Generalized Neural Architecture Performance Estimator, which is pretrained on open neural architecture benchmarks, and aims to generalize to completely unseen architectures through combined innovations in network representation, contrastive pretraining, and fuzzy clustering-based predictor ensemble.
AIO-P: Expanding Neural Performance Predictors Beyond Image Classification
Evaluating neural network performance is critical to deep neural network design but a costly procedure.
One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation
CAT-ART boosts the recommendation performance in any target domain through the combined use of the learned global user representation and knowledge transferred from other domains, in addition to the original user embedding in the target domain.
Mulco: Recognizing Chinese Nested Named Entities Through Multiple Scopes
Nested Named Entity Recognition (NNER) has been a long-term challenge to researchers as an important sub-area of Named Entity Recognition.
R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning
Systematicity, i. e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical to machine intelligence.
RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation
The learned GUR captures the overall preferences and characteristics of a user and thus can be used to augment the behavior data and improve recommendations in any single domain in which the user is involved.
TAG: Toward Accurate Social Media Content Tagging with a Concept Graph
Although conceptualization has been widely studied in semantics and knowledge representation, it is still challenging to find the most accurate concept phrases to characterize the main idea of a text snippet on the fast-growing social media.
A Transferable General-Purpose Predictor for Neural Architecture Search
Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS).
L$^{2}$NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning
In this paper, we propose L$^{2}$NAS, which learns to intelligently optimize and update architecture hyperparameters via an actor neural network based on the distribution of high-performing architectures in the search history.
Profiling Neural Blocks and Design Spaces for Mobile Neural Architecture Search
Neural architecture search automates neural network design and has achieved state-of-the-art results in many deep learning applications.
LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization
Language model pre-training based on large corpora has achieved tremendous success in terms of constructing enriched contextual representations and has led to significant performance gains on a diverse range of Natural Language Understanding (NLU) tasks.
Similarity Embedding Networks for Robust Human Activity Recognition
Deep learning models for human activity recognition (HAR) based on sensor data have been heavily studied recently.
Verdi: Quality Estimation and Error Detection for Bilingual Corpora
Verdi adopts two word predictors to enable diverse features to be extracted from a pair of sentences for subsequent quality estimation, including a transformer-based neural machine translation (NMT) model and a pre-trained cross-lingual language model (XLM).
Meta-HAR: Federated Representation Learning for Human Activity Recognition
However, the effectiveness of federated learning for HAR is affected by the fact that each user has different activity types and even a different signal distribution for the same activity type.
Generative Adversarial Neural Architecture Search
Despite the empirical success of neural architecture search (NAS) in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to assess.
Generative Adversarial Neural Architecture Search with Importance Sampling
Despite the empirical success of neural architecture search (NAS) algorithms in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to be assessed.
QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications
In this paper, we present QBSUM, a high-quality large-scale dataset consisting of 49, 000+ data samples for the task of Chinese query-based document summarization.
Neural Architecture Search For Keyword Spotting
Deep neural networks have recently become a popular solution to keyword spotting systems, which enable the control of smart devices via voice.
Ranked #1 on
Keyword Spotting
on Google Speech Commands
(Google Speech Commands V1 6 metric)
Reinforced Curriculum Learning on Pre-trained Neural Machine Translation Models
Specifically, we propose a data selection framework based on Deterministic Actor-Critic, in which a critic network predicts the expected change of model performance due to a certain sample, while an actor network learns to select the best sample out of a random batch of samples presented to it.
GIANT: Scalable Creation of a Web-scale Ontology
These services will benefit from a highly structured and web-scale ontology of entities, concepts, events, topics and categories.
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions.
Learning Privately over Distributed Features: An ADMM Sharing Approach
Distributed machine learning has been widely studied in order to handle exploding amount of data.
A User-Centered Concept Mining System for Query and Document Understanding at Tencent
We further present our techniques to tag documents with user-centered concepts and to construct a topic-concept-instance taxonomy, which has helped to improve search as well as news feeds recommendation in Tencent QQ Browser.
Learning to Generate Questions by Learning What not to Generate
In CGC-QG, we design a multi-task labeling strategy to identify whether a question word should be copied from the input passage or be generated instead, guiding the model to learn the accurate boundaries between copying and generation.
Multiresolution Graph Attention Networks for Relevance Matching
In this paper, we are especially interested in relevance matching between a piece of short text and a long document, which is critical to problems like query-document matching in information retrieval and web searching.
Stochastic Distributed Optimization for Machine Learning from Decentralized Features
Distributed machine learning has been widely studied in the literature to scale up machine learning model training in the presence of an ever-increasing amount of data.
A Model Parallel Proximal Stochastic Gradient Algorithm for Partially Asynchronous Systems
We prove that AsyB-ProxSGD achieves a convergence rate of $O(1/\sqrt{K})$ to stationary points for nonconvex problems under \emph{constant} minibatch sizes, where $K$ is the total number of block updates.
Android Malware Detection using Large-scale Network Representation Learning
With the growth of mobile devices and applications, the number of malicious software, or malware, is rapidly increasing in recent years, which calls for the development of advanced and effective malware detection approaches.
Cryptography and Security
Android Malware Detection based on Factorization Machine
As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications.
Cryptography and Security
Matching Natural Language Sentences with Hierarchical Sentence Factorization
The proposed sentence factorization technique leads to the invention of: 1) a new unsupervised distance metric which calculates the semantic distance between a pair of text snippets by solving a penalized optimal transport problem while preserving the logical relationship of words in the reordered sentences, and 2) new multi-scale deep learning models for supervised semantic training, based on factorized sentence hierarchies.
Growing Story Forest Online from Massive Breaking News
We describe our experience of implementing a news content organization system at Tencent that discovers events from vast streams of breaking news and evolves news story structures in an online fashion.
Ranked #3 on
Information Threading
on NewSHead
Asynchronous Stochastic Proximal Methods for Nonconvex Nonsmooth Optimization
We study stochastic algorithms for solving nonconvex optimization problems with a convex yet possibly nonsmooth regularizer, which find wide applications in many practical machine learning applications.
A Block-wise, Asynchronous and Distributed ADMM Algorithm for General Form Consensus Optimization
Many machine learning models, including those with non-smooth regularizers, can be formulated as consensus optimization problems, which can be solved by the alternating direction method of multipliers (ADMM).
Matching Article Pairs with Graphical Decomposition and Convolutions
Identifying the relationship between two articles, e. g., whether two articles published from different sources describe the same breaking news, is critical to many document understanding tasks.
Expectile Matrix Factorization for Skewed Data Analysis
Matrix factorization is a popular approach to solving matrix estimation problems based on partial observations.
